Integrated pathway analysis of altered driver genes in adenoid cystic carcinoma Project Summary/Abstract Salivary gland adenoid cystic carcinoma (ACC) is an unusual malignancy with an unpredictable clinical behavior. While local and regional control can be obtained with surgery and radiation therapy, up to 50% of patients may develop distant metastasis to the lung or bone. The presence of metastatic deposits can sometimes portend a poor prognosis, but it is not uncommon for these to remain relatively dormant and asymptomatic for years. Thus, the identification of systemic agents as adjunctive treatment would be ideal, as there would likely be ample opportunity to target this deadly disease. Currently, there are no well accepted chemotherapeutic or targeted agents for use in ACC. Unfortunately, the biologic basis for ACC development is poorly understood. Because ACC is not smoking- related, and there is no familial association or known exposure risk profile, it is believed that there must be common, spontaneous alterations that exist to explain how it arises. It likely that the model for ACC carcinogenesis will involve a unique pattern or set of genes, and, therefore, we cannot rely on simply evaluating known tumor-related genes involved in other cancers. Our lab has focused on the identification of and screening for epigenetic changes in ACC. We believe that with the newer generation of whole genome sequencing and with more robust bioinformatic approaches, we can rapidly identify novel genetic and epigenetic alterations involved in ACC. Accordingly, our specific aims are: 1) To perform multiplatform whole-genome analysis including whole genome methylation profiling, RNA sequencing, exome sequencing, and SNP array, 2) Integrative pathway analysis using cancer outlier Gene Profile Sets (coGPS), 3) Validation of pathway analysis and functional analysis of relevant driver gene targets. By employing the newest whole genome studies in matched primary ACC samples, we will gain a better understanding of the relationship between epigenetic changes, mutations, copy number variations, translocations, and splice variants. The novel coGPS methodology also allows for pathway analysis, thereby further integrating the data and highlighting the molecular changes that are involved in ACC. Lastly, we already have the tools and biologic material for the validation of identified genes to confirm their role in ACC. At the conclusion of this work, we wil have generated a tremendous data set that can be mined in a number of different ways for the benefit of the entire research community. As such, all data sets will be deposited into the Gene Expression Omnibus (GEO) Database for public use. Our particular interest will focus on a bioinformatic approach that highlights the driver genes in a pathway-specific manner. Ultimately, it is these types of studies that allow for rapid advancement within the field and identification o drug-able targets that have the potential to positively affect patient care in this understudied disease.